VideoForensicsHQ: Detecting High-quality Manipulated Face Videos
This addresses the challenge of video forgery detection for security and media integrity, but is incremental as it builds on prior detection methods with improved features.
The paper tackles the problem of detecting high-quality manipulated face videos by introducing a new benchmark dataset of unprecedented quality, and demonstrates that existing detectors struggle with fakes that fool the human eye, leading to a new family of detectors that outperform existing methods in accuracy and generalization.
There are concerns that new approaches to the synthesis of high quality face videos may be misused to manipulate videos with malicious intent. The research community therefore developed methods for the detection of modified footage and assembled benchmark datasets for this task. In this paper, we examine how the performance of forgery detectors depends on the presence of artefacts that the human eye can see. We introduce a new benchmark dataset for face video forgery detection, of unprecedented quality. It allows us to demonstrate that existing detection techniques have difficulties detecting fakes that reliably fool the human eye. We thus introduce a new family of detectors that examine combinations of spatial and temporal features and outperform existing approaches both in terms of detection accuracy and generalization.